Dataverse World Agroforestry (ICRAF)
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Land and Soil Health Assessment in the Western Africa Sentinel Landscape
No full textThe LDSF was carried out at two-100 km2 sites within the West Africa Sentinel Landscape: Cassou and Koungoussi in Burkina Faso. Field teams were trained by Jerome Tondoh. Field surveys were completed in March 2014.
The LDSF is a spatially stratified, randomized sampling design, developed to provide a biophysical baseline at landscape level and a monitoring and evaluation framework for assessing processes of land degradation and effectiveness of rehabilitation measures over time.
Measured variables include: land cover, tree and shrub densities, tree biodiversity, erosion prevalence, infiltration capacity, along with an assessment of impact to habitat and occurrence of soil conservation structures. Soil samples were also collected (320 top (0-20 cm) and sub (20-50 cm) soil samples per site) and were processed in Burkina Faso. Processed samples were shipped to Nairobi and subjected to infrared spectroscopy and wet chemistry analysis. These combined data sets will be used to assess soil and ecosystem health for the landscape in more detail
Commercial Forestry Investment Sub-Project (CFISP) barometer: A rapid assessment of the quality of INREMP CFISP models (Lake Lanao River Basin)
No full textThe CFISP barometer is developed to provide a systematic way of analyzing, understanding, and documenting the implementation of the CFISPs at the household level, as well as in the wider context of the river basin where is it located. The developed framework aims to provide an evaluation of the quality of the CFISPs to guide ICRAF to develop the necessary interventions as specified in its technical assistance contract
Replication Data for Soil macrofauna collected at LDSF Sentinel Sites
No full textSoil macrofauna collected at 4 LDSF sentinel sites using the TSBF monolith method. Soil analyses conducted on monolith soil also included
Carbon neutral? No change in mineral soil carbon stock under oil palm plantations derived from forest or non-forest in Indonesia. Agric. Ecosyst. Environ. (2015). http://dx.doi.org/10.1015/j.agee.2015.06.0009
No full textSustainability criteria for palm oil production guide new planting towards non-forest land cover on mineral soil, avoiding carbon debts caused by forest and peat conversion. Effects on soil carbon stock (soil Cstock) of land use change trajectories from forest and non-forest to oil palm on mineral soils include initial decline and subsequent recovery, however modeling efforts and life-cycle accounting are constrained by lack of comprehensive data sets; only few case studies underpin current debate. We analyzed soil Cstock (Mg ha-1), soil bulk density (BD, g cm-3) and soil organic carbon concentration (Corg, %) from 155 plots in 20 oil palm plantations across the major production areas of Indonesia, identifying trends during a production cycle on 6 plantations with sufficient spread in plot age. Plots were sampled in four management zones: weeded circle (WC), interrow (IR), frond stacks (FS), and harvest paths (HP); three depth intervals 0-5, 5-15 and 15-30 cm were sampled in each zone. Compared to the initial condition, increases in Corg (16.2%) and reduction in BD (8.9%) in the FS zone, was compensated by decrease in Corg (21.4%) and increase in BD (6.6%) in the HP zone, with intermediate results elsewhere. For a weighted average of the four management zones and after correction for equal mineral soil basis, the net temporal trend in soil Cstock in the top 30 cm of soil across all data was not significantly different from zero in both forest- and non-forest-derived oil palm plantations. Individual plantations experienced net decline, net increase or U-shaped trajectories. The 2% difference in mean soil Cstock in forest and non-forest derived oil palm plantations was statistically significant (p<0.05). Unless soil management changes strongly from current practice, it is appropriate for C footprint calculations to assume soil Cstock neutrality on mineral soils used for oil palm cultivation
Building Biocarbon and Rural Development in West Africa (BIODEV), WP1.3
No full textThe Building Biocarbon and Rural Development in West Africa Programme aims to demonstrate the multiple developmental and environmental wins that result from a high value biocarbon approach to climate change and variability in large landscapes principally in Mali, Sierra Leone and Guinea. The Programme will also build local institutions and capacity to be able to sustain the benefits in the sites and will establish linkages with related initiatives to jointly build national and regional capacity to scale up the approaches into other programmes and projects. The themes of the Programme are very closely linked to Finland's international development priorities and are closely aligned with the priorities expressed in its national poverty reduction and climate change adaptation strategies. Furthermore, the Programme aims to generate critical information that can fill the global knowledge gaps on how to better link climate change mitigation and adaptation thrusts and how to make these actions work effectively to enhance the livelihoods of rural communities. (2015
Baseline dataset on uptake of sustainable land management practices, adherence to agroforestry concession guidelines and other household variables
No full textThis dataset is the baseline of the project Piloting approaches to rural advisory services in support of scaling of the Agroforestry Concessions scheme in Peru (PARA), which is currently being implemented by ICRAF (World Agroforestry). This project aims to determine which Rural Advisory Services (RAS) model generates greater adherence and permanence to the agroforestry concessions that the Peruvian state intends to implement. For this purpose, it will compare two treatments and a control group.
The dataset unit of analysis is household and contains a total of 1,070 observations of 45 villages. The database includes general information on the agricultural unit (size, tenure, land use, problems, practices, and innovations), household income composition, access to agricultural services, intra-household dynamics, etc. In addition, information on the trees and forest products derived from each land use (crops, pastures, pastures, forest plantations, and forests) is detailed. The database is composed of 6 files, 1 file at the household level and 5 files at the level of each of the land uses
Farm Characterization Survey - Kenya
No full textThis is data collected for the Farm Characterization Survey done in Kenya
Fruiting Africa Endline Consumption and Nutrition Survey
No full textFruiting Africa Consumption and Nutrition Endline Survy
Planejamento e Avaliação para Tomada de Decisão em Sistemas Agroflorestais - PLANTSAFS, Pipiripau, Distrito Federal, Brasil
No full textConjunto de dados coletados em campo para estudo do contexto no desenvolvimento de opções agroflorestais e recomendações agroecológicas através da ferramenta PLANTSAFS (planejamento e avaliação para tomada de decisão em sistemas agroflorestais). Estudo de caso no Pipiripau, Distrito Federal, Brasil
World Economic Fruit Tree Species
No full textA list of 688 fruit taxa that was downloaded on 18 June 2020 from GRIN-global for World Economic Plants in the database for the query: family = 'all families' & native country = 'all countries' & economic uses: Human food = ''as fruit'. Plant names were standardized via the WorldFlora package (version 1.6; Kindt 2020) with the World Flora Online Taxonomic Backbone version 2019.05. A species list was created from the genus and species names of the matched taxonomic name. From this list, a list of 404 fruit tree species that was derived by matching species names with the GlobalTreeSearch (GTS) database (version 1.3; Beech et al. 2017). Continents where species are native follow the World Geographic Scheme of Recording Plant Distributions (WGSRPD). Countries listed in GTS were matched with continents of the WGSRPD by using country-continent allocations available from Kindt 2020. Listing in USDA Food Composition Databases and Global Invasive Species Database were obtained from the Agroforestry Species Switchboard. Listing in FAO crop statistics was inferred from World Yield Data for 2018, downloaded in March 2020